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It's both. I've consulted in many situations where people make horrible business decisions based on "statistically significant." People mistake p-values for p(
by happy3483t583 3y ago
It's both.
I've consulted in many situations where people make horrible business decisions based on "statistically significant." People mistake p-values for p(hypothesis|data), mistake p-values for effect size ("it's highly significant!"); moreover, people using NHST don't understand multiplicity or "topping-off" problems.
Of course one could argue that they just need to review Intro Stats, but that skips over the impenetrable conceptual nature of p-values and NHST. Given that regular people must use applied stats and given that NHST is fundamentally arcane and confusing, then the stage is set for endless drama.
Now if we were sampling manufactured products from batches and looking for a quantitative upper-bound on the failure rates, run over many batches, then we have a winner!
- crispyambulance 3y agoIt seems that the problems you cited are still very much people-related. There's a lot of pressure (especially in regulated environments) to be "data-driven". It's tempting for folks to pick up a tool like minitab and churn some stats, cargo-cult-style, to prove whatever and look-smart while doing it. This is tricky stuff. Nobody like to admit they're confused or that the path isn't clear. I agree it sets the stage for drama and failure.